Because the internal representation learned by a CNN may be influenced by the learning algorithm, four different loss functions commonly used in imbalanced learning are tested: cross-entropy (CE) and three cost-sensitive methods - ASL, Focal Loss, and LDAM. In addition, to assess the impact of over-sampling at different points in the training process and as baselines to EOS, three popular over-sampling algorithms are used: SMOTE, Borderline-SMOTE and Balanced SVM. Additionally, we use three state-of-the-art GAN-based oversampling methods: GAMO [1]}, BAGAN [2]}, and CGAN [3]}.
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